1. Coding of Selection Criteria for Cancer Treatment Plans Savvas Nikiforou

2. Automated Matching of Patients to Clinical Trials Part of the project:

3. Research Group Faculty –Lawrence Hall –Dmitry Goldgof –Eugene Fink Students –Lynn Fletcher –Princeton Kokku –Savvas Nikiforou –Rochelle Harris

4. Motivation Selecting among treatment plans Minimizing pain and cost of the selection process Reducing the physician’s effort

5. Expert System Guides the nurse through related questions Identifies the appropriate medical tests

6. Outline of the Talk Eligibility decisions Knowledge base Input of the knowledge Demonstration

7. Related Work Initial expert system Fletcher, Hall and Goldgof, 1999 Minimizing pain and cost Kokku, Hall and Goldgof, 2001

8. Example: Selection Criteria Female, not older than 50 Breast cancer, stage II No prior surgery

9. Example: Questions Sex: Age: Female Male 35

10. Example: Conclusion Patient is not eligible

11. Example: Questions Sex: Age: Female Male 35

12. Example: Questions Cancer stage: Prior surgery? Yes No Unknown I II III IV

13. Example: Conclusion Patient is eligible

14. Full Functionality Orders and groups the questions Considers multiple treatment plans

15. Old System A programmer has to code the questions

16. New System A programmer has to code the questions A nurse enters the questions through a friendly interface Problem: Build the interface

17. Outline of the Talk Eligibility decisions Knowledge base Input of the knowledge Demonstration

18. Main Objects Questions Tests Eligibility criteria

19. Types of Questions Yes / No / Unknown Multiple choice Numeric

20. Examples of Questions Prior surgery? Yes No Unknown

21. Examples of Questions Prior surgery? Yes No Unknown Cancer stage: I II III IV

22. Examples of Questions Prior surgery? Yes No Unknown Cancer stage: I II III IV Age:

23. Tests A medical test answers several questions. It involves certain pain and cost.

24. Example Test: Name and Cost Test Name: Cost: 42.23 Pain:1 Blood test

25. Example Test: Questions Yes / No Question: Histologically proven breast cancer?

26. Example Test: Questions Multiple choice Question: Options: Patient’s clinical state T-1 T-2 T-3

27. Example Test: Questions Numeric Question: White cell blood count 0 500000 0 MinMaxPrec

28. Eligibility Criteria A logical expression that determines eligibility for a specific treatment

29. Example: Criteria AND White Blood Count >100,000 No Heart Related Problems OR Cancer Stage I Cancer Stage II

30. Outline of the Talk Eligibility decisions Knowledge base Input of the knowledge Demonstration

31. Input Questions Tests Eligibility criteria

32. Input: Test Name: Cost: Pain: Type test name here $$$$.$$ 0-5

33. Input: Question Format Text Type Yes / NoMultiple ChoiceNumeric

34. Input: Yes / No Question Yes / No Is patient’s age less than 64? Text Type

35. Input: Multiple Choice Question Multiple Choice Patient’s age Text Type Options < 20 20 to 40 41 to 60 > 60

36. Input: Numeric Question Numeric Patient’s age Text Type MinMaxPrec. 1200 0

37. YesNo Input: Eligibility Logical structure AND OR Questions Sex Female Male Age Tumors? Lesions? YesNo Eligibility answers 4065 FromTo

38. Outline of the Talk Eligibility decisions Knowledge base Input of the knowledge Demonstration

39. Current System Online Demo

40. Main results Formal model of selection criteria and related optimization problems Friendly input interface Visual representation of these criteria

41. Coming soon Completion of the interface Converting the input knowledge into internal logical structures